Analysis of Public Opinion on Religion and Politics in Indonesia using K-Means Clustering and Vader Sentiment Polarity Detection - Mustaqim, Tanzilal (Proceeding International Conference on Science and Engineering, 2020)
Religion and politics are two things that are closely related to each other and cannot be separated. Various public responses expressed by various public media such as print media and social media that can be classified as positive, neutral and negative, one of which is using Twitter. Twitter is a microblogging social media that contains many writings with many types from various types of users including posts that contain opinions about religion and politics. This research conducted an analysis process in the form of extraction of hidden insight data, visual analysis and sentiment analysis of public opinion related to religion and politics. The analysis was conducted on 5433 datasets written on Twitter on November 12, 2019. The analysis process began with data pre-processing, data clustering and sentiment analysis. Pre-processing data generates clean data from characters and non-essential data for use in the process of data clustering and sentiment analysis. Data clustering produces extraction of hidden insight data using k-means clustering. Sentiment data analysis uses vader sentiment polarity detection to determine dataset sentiments. The results of tests carried out using jupyter notebook show insight data hidden in the form of 50 unique words that are divided into 5 clusters of 10 words each then the sentiment analysis process is carried out in each cluster. Another result is visual analysis in the form of word cloud and hashtag clustering which shows the dominant words of each piece of data according to sentiment and word count. Also pointed out words that have a frequency of dominant emergence accompanied by word sentiments. The process of analyzing public opinion datasets related to religion and politics using k-means clustering and vader polarity detection sentiments can be done well.